Methods and apparatuses for processing images of particles in a fluid sample are well known. For example, U.S. Pat. Nos. 4,667,335 and 4,612,614 describe apparatuses having a software program that determines various characteristics of particles (e.g., biological particles) by using an imaging signal. The apparatuses disclosed in these references can automatically—i.e., without human intervention—determine characteristics such as color, size, and brightness of particles in a fluid sample. Moreover, based on the determined characteristics, these apparatuses can categorize each particle into one of many classes and calculate the concentration of each particle type (i.e., particle class). This automatic sample analysis and concentration determination process is referred to as Auto-Particle Recognition (APR).
For practical reasons, a limit is usually imposed on the amount of sample that is analyzed. In one conventional apparatus, the sample is analyzed one portion at a time such that a first portion is analyzed, the number of particles in that portion is counted, a next portion is then analyzed, the number of particles in that portion is added to the total count, etc. This portion-by-portion particle counting process continues until a maximum time period passes, maximum total sample volume is reached, or a maximum number of particles is counted.
The classification and calculation results are typically displayed in the manner similar to that disclosed in U.S. Pat. No. 5,822,447. Namely, a plurality of optical frames are taken, wherein each frame is a picture of a portion of the sample. Preferably, the frames represent different portions of the sample. A frame is made of one or more “patches” of images, with each patch containing at least one particle image. The patches are classified into one of a plurality of classes based on the images they contain, and the classes are usually characterized by one or more visually discernible characteristics. In some embodiments, if a patch contains more than one discernable particle image, the particle images could be classified separately. In other embodiments, the image of the more predominant particle is used to classify the patch. After the classification, the concentrations of each class of particles are determined.
The patches extracted from the frames are displayed on a graphical user interface (e.g., a computer monitor), preferably in an ordered array by classification. The number of particles within each class, or any parameter derived therefrom (e.g., a percentage of the total number of particles), may be displayed. The APR process determines the concentration of each particle type (i.e., particle class) based on this classification. Then, an operator manually reviews the APR classification results and corrects any errors. During the manual review process, the operator may pull a misclassified particle out of one class and add it to another class.
There are a few different modes of particle classification, and the mode that is used affects the way in which the operator conducts the manual review. In the complete classification mode, all the particles in a sample are individually classified. The manual review process that follows the complete classification is a Full Edit mode of review whereby the operator manually checks each individual classified particle image to ensure proper classification. During the Full Edit process, the operator reclassifies misclassified particle images into the proper particle class. While the Full Edit mode is advantageous in that every resulting classification is one that has been manually “approved” by the operator and therefore highly likely to be accurate, it is very time consuming for the operator. Thus, the complete particle classification and the Full Edit mode are preferably used with a sample that contains a relatively small number of particles (e.g., less than 1000 particles).
In the partial classification mode, which is described in more detail in U.S. Pat. No. 6,141,624, the operator reviews only a subset of the particle images. Of the I number of particles in the sample, at most NMAX particles are selected for operator review and classified (NMAX<I). Since the operator does not review all the I particles, the average review time required of the operator can be significantly reduced relative to the complete classification mode. The manual review and edit process that follows the partial classification is sometimes referred to as being in the Abbreviated Edit mode. The partial classification and Abbreviated Edit mode are ideal for larger samples containing thousands and even tens of thousands of particles.
The complete classification yields an accurate result but demands a lot of time from the operator. The partial classification demands less time from the operator but the accuracy may be compromised. A method that will help the operator save time in the complete classification mode and improve the accuracy of the partial classification mode is desired.